Presently, the wireless spectrum is being used aggressively and dynamically by a great number of wireless systems, such as mobile telephone systems, wireless local and personal networks, wireless WAN, and WiMAX systems. Accordingly, the propagation-friendly frequency spectrum, ranging from a few kHz to several GHz, is either fully occupied by licensed systems (e.g., cellular networks) or overly interference-limited by unlicensed systems (e.g., Bluetooth). This has motivated recent research into robust spectrum sensing and dynamic spectrum use in the context of cognitive radios and software-defined radios. With continuing advances in CMOS technology, wideband sampling methods apply sophisticated digital signal processing techniques in order to improve the spectrum use. For instance, sub-Nyquist sampling is a technique which includes sampling a wideband signal with a sampling rate lower than Nyquist rate and processing it in digital domain. As described in M. Mishali and Y. C. Eldar, “From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals,” IEEE J. of Selected Topics in Signal Proc., Vol. 4, pp. 375-391, April 2010 (“Mishali et al.”), for example, a new paradigm of wideband sampling has been proposed, which is incorporated herein by reference in its entirety. Devices in accordance with this technique may sample a wideband signal in a blind manner, i.e., without a priori knowledge of the center frequencies of the narrowband signal that make up the wideband signal.
As described in Mishali et al., an analog wideband signal having unknown spectral support may be blindly sampled by first multiplying it by signals from a bank of periodic waveforms. The product is then low-pass filtered, amplified, and sampled by an analog-to-digital converter (ADC) at a low rate, which is orders of magnitude smaller than the Nyquist rate. Once the unknown spectral support of the wideband signal is identified, the continuous signals may be reconstructed using closed-form expressions based on the periodic waveforms that were used to sample the signal.
The blind wideband sampling technique discussed above does not assume any channel selection filtering before the ADCs sample each component; each component is processed with the same amplifier gain. Accordingly, a significant power variation among the narrowband signals comprising the wideband signal, for example, in the presence of strong interferers, such as adjacent-channel interferers or out-of-band interferers, may degrade the quality of recovered signals. For instance, a strong interferer may saturate one or more of the ADCs, and thus, the following digital signal processing (e.g., recovery) cannot operate properly.
This problem could be addressed by reducing the gain of the amplifiers; however, the noise of the ADC would become dominant compared to the desired signals. Because the known techniques assume that the wideband signal is formed of narrowband signals of comparable power, the signal cannot be sampled properly with conventional ADCs at a reasonably high signal-to-noise ratio (SNR).
Accordingly, there is a need for a method and device for blind wideband sampling that can correctly recover narrowband signals using conventional ADCs and achieve acceptable SNR values.